Background: Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning.
Methods: We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification.
Results: We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy.
Conclusion: Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.
Keywords: EEG microstate; Gender difference; Gender recognition; Machine learning.
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